This book presents a systematic study of an emerging field in the development of multi-agent systems. In a wide spectrum of applications, it is now common to see that multiple agents work cooperatively to accomplish a complex task. The book assists the implementation of such applications by promoting the ability of multi-agent systems to track - using local communication only - the mean value of signals of interest, even when these change rapidly with time and when no individual agent has direct access to the average signal across the whole team; for example, when a better estimation/control performance of multi-robot systems has to be guaranteed, it is desirable for each robot to compute or track the averaged changing measurements of all the robots at any time by communicating with only local neighboring robots. The book covers three factors in successful distributed average tracking: algorithm design via nonsmooth and extended PI control; distributed average tracking for double-integrator, general-linear, Euler-Lagrange, and input-saturated dynamics; and applications in dynamic region-following formation control and distributed convex optimization. The book presents both the theory and applications in a general but self-contained manner, making it easy to follow for newcomers to the topic. The content presented fosters research advances in distributed average tracking and inspires future research directions in the field in academia and industry.

This book introduces the application of nonlinear dynamics theory for driving system of electric vehicle and hybrid electric vehicle respectively. It establishes the dynamic models for driving system of electric vehicle and hybrid electric vehicle under various working conditions. And the nonlinear dynamics theory is applied to the qualitative analysis and quantitative calculation for the models. The theoretical analysis results are applied to guide the optimization of control strategies. In the end of each chapter, corresponding simulations or experiments are provided to verify the corresponding instances which are carefully selected. This book will give some guidance to readers when they deal with nonlinear dynamics problems of vehicles in the future and provide theoretical bases for the further study of the nonlinear dynamics for driving system of electric vehicle and hybrid electric vehicle. The book is written for engineer of electric vehicle and hybrid vehicle, teachers and students majoring in automobile and automation.

This book presents the latest researches on hypersonic steady glide dynamics and guidance, including the concept of steady glide reentry trajectory and the stability of its regular perturbation solutions, trajectory damping control technique for hypersonic glide reentry, singular perturbation guidance of hypersonic glide reentry, trajectory optimization based on steady glide, linear pseudospectral generalized nominal effort miss distance guidance, analytical entry guidance and trajectory-shaping guidance with final speed and load factor constraints. They can be used to solve many new difficult problems in entry guidance. And many practical engineering cases are provided for the readers for better understanding. Researchers and students in the fields of flight vehicle design or flight dynamics, guidance and control could use the book as valuable reference.

This book describes a user-friendly, evolutionary algorithms-based framework for estimating data-driven models for a wide class of dynamical systems, including linear and nonlinear ones. The methodology addresses the problem of automating the process of estimating data-driven models from a user's perspective. By combining elementary building blocks, it learns the dynamic relations governing the system from data, giving model estimates with various trade-offs, e.g. between complexity and accuracy. The evaluation of the method on a set of academic, benchmark and real-word problems is reported in detail. Overall, the book offers a state-of-the-art review on the problem of nonlinear model estimation and automated model selection for dynamical systems, reporting on a significant scientific advance that will pave the way to increasing automation in system identification.

This third edition of Statistical Physics of Complex Systems has been expanded to provide more examples of applications of concepts and methods from statistical physics to the modeling of complex systems. These include avalanche dynamics in materials, models of social agents like road traffic or wealth repartition, the real space aspects of biological evolution dynamics, propagation phenomena on complex networks, formal neural networks and their connection to constraint satisfaction problems. This course-tested textbook provides graduate students and non-specialists with a basic understanding of the concepts and methods of statistical physics and demonstrates their wide range of applications to interdisciplinary topics in the field of complex system sciences, including selected aspects of theoretical modeling in biology and the social sciences. It covers topics such as non-conserved particles, evolutionary population dynamics, networks, properties of both individual and coupled simple dynamical systems, and convergence theorems, as well as short appendices that offer helpful hints on how to perform simple stochastic simulations in practice. The original spirit of the book is to remain accessible to a broad, non-specialized readership. The format is a set of concise, modular, and self-contained topical chapters, avoiding technicalities and jargon as much as possible, and complemented by a wealth of worked-out examples, so as to make this work useful as a self-study text or as textbook for short courses.

This book offers fundamental information on the analysis and synthesis of continuous and sampled data control systems. It includes all the required preliminary materials (from mathematics, signals and systems) that are needed in order to understand control theory, so readers do not have to turn to other textbooks. Sampled data systems have recently gained increasing importance, as they provide the basis for the analysis and design of computer-controlled systems. Though the book mainly focuses on linear systems, input/output approaches and state space descriptions are also provided. Control structures such as feedback, feed forward, internal model control, state feedback control, and the Youla parameterization approach are discussed, while a closing section outlines advanced areas of control theory. Though the book also contains selected examples, a related exercise book provides Matlab/Simulink exercises for all topics discussed in the textbook, helping readers to understand the theory and apply it in order to solve control problems. Thanks to this combination, readers will gain a basic grasp of systems and control, and be able to analyze and design continuous and discrete control systems.

With the development of Big Data platforms for managing massive amount of data and wide availability of tools for processing these data, the biggest limitation is the lack of trained experts who are qualified to process and interpret the results. This textbook is intended for graduate students and experts using methods of cluster analysis and applications in various fields. Suitable for an introductory course on cluster analysis or data mining, with an in-depth mathematical treatment that includes discussions on different measures, primitives (points, lines, etc.) and optimization-based clustering methods, Cluster Analysis and Applications also includes coverage of deep learning based clustering methods. With clear explanations of ideas and precise definitions of concepts, accompanied by numerous examples and exercises together with Mathematica programs and modules, Cluster Analysis and Applications may be used by students and researchers in various disciplines, working in data analysis or data science.

The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, trafficand intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.

This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those who want to gain a deeper insight in the wide range of distributed MPC techniques available.

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This book focuses on modern technologies and systems for solving problems in the energy sector. It is shown that bioenergy is one of the promising areas of energy development. The book collected the experience of scientists from many countries in the research of renewable energy. The advantages of renewable energy are general availability, renewability, environmental friendliness. The analysis carried out by the authors shows the current state of renewable energy in the world, its trends and prospects. New measuring systems are presented, which can become the basis for measuring the thermal characteristics of various types of fuels, including biofuels, insulating materials, enclosing structures, etc. System for monitoring of grainy biomass comminution with the use of genetic algorithms has been presented and described. New technologies for the construction of power plants based on renewable energy sources have been proposed and investigated.

Iterative Learning Control (ILC) differs from most existing control methods in the sense that, it exploits every possibility to incorporate past control informa tion, such as tracking errors and control input signals, into the construction of the present control action. There are two phases in Iterative Learning Control: first the long term memory components are used to store past control infor mation, then the stored control information is fused in a certain manner so as to ensure that the system meets control specifications such as convergence, robustness, etc. It is worth pointing out that, those control specifications may not be easily satisfied by other control methods as they require more prior knowledge of the process in the stage of the controller design. ILC requires much less information of the system variations to yield the desired dynamic be haviors. Due to its simplicity and effectiveness, ILC has received considerable attention and applications in many areas for the past one and half decades. Most contributions have been focused on developing new ILC algorithms with property analysis. Since 1992, the research in ILC has progressed by leaps and bounds. On one hand, substantial work has been conducted and reported in the core area of developing and analyzing new ILC algorithms. On the other hand, researchers have realized that integration of ILC with other control techniques may give rise to better controllers that exhibit desired performance which is impossible by any individual approach."

This book introduces iterative learning control (ILC) and its applications to the new equations such as fractional order equations, impulsive equations, delay equations, and multi-agent systems, which have not been presented in other books on conventional fields. ILC is an important branch of intelligent control, which is applicable to robotics, process control, and biological systems. The fractional version of ILC updating laws and formation control are presented in this book. ILC design for impulsive equations and inclusions are also established. The broad variety of achieved results with rigorous proofs and many numerical examples make this book unique. This book is useful for graduate students studying ILC involving fractional derivatives and impulsive conditions as well as for researchers working in pure and applied mathematics, physics, mechanics, engineering, biology, and related disciplines.

This intriguing book was born out of the many discussions the authors had in the past 10 years about the role of scale-free structure and dynamics in producing intelligent behavior in brains. The microscopic dynamics of neural networks is well described by the prevailing paradigm based in a narrow interpretation of the neuron doctrine. This book broadens the doctrine by incorporating the dynamics of neural fields, as first revealed by modeling with differential equations (K-sets). The book broadens that approach by application of random graph theory (neuropercolation). The book concludes with diverse commentaries that exemplify the wide range of mathematical/conceptual approaches to neural fields. This book is intended for researchers, postdocs, and graduate students, who see the limitations of network theory and seek a beachhead from which to embark on mesoscopic and macroscopic neurodynamics.

`While there is much noise about the control of money laundering, there are few whose work is able to rise above the din and in clear notes contribute in a constructive manner to the debate. This work is not only an intelligent discussion of many of the substantive issues relating to the control of money laundering, but a great deal more. Drawing on systems theory and seeing the control of money laundering particularly from the standpoint of technology as complex and integral to the proper and effective operation of financial institutions, the author addresses in a novel and practical way the design and management of risk based compliance. The theory is tested, not only in terms of viable technology, but also in an actual case study involving real issues in a bank. Consequently, those concerned with the formulation of policy, the design of controls and procedures and the implementation of such will find the contribution that this book makes of great significance.' - Barry Rider, Bryan Cave LLP, UK`This book avoids the usual trap of interminably listing AML war stories. Instead Dr. Demetis presents a solid theoretical foundation for AML research and practice. He gives a damning critique of the way so-called technological solutions are used uncritically by some AML professionals, and analyses the risk-based approach, describing its problems and ways of avoiding them. He presents a fascinating in-depth case study of a financial institution, and a short case of a bank using technology to improve its True Positive Rate substantially to 17 per cent.' - Ian Angell, London School of Economics, UK `Dr Demetis makes a great contribution to our understanding of anti-money laundering at both a systems and practical level. [He] writes as someone who not only thinks deeply about these issues but, as the in-depth case examples show, has tried to see how far technology can address some audacious goals. Readers will learn that while risk-based approaches to anti-money laundering have been an interesting regulatory development, practical implementation, despite the hype of technology vendors, is "still at a primordial state".' - Michael Mainelli, Gresham College and Z/YEN Group, UK This insightful book examines the influence of information systems on anti-money laundering (AML). It builds on systems theory in order to develop a coherent theoretical framework that can be used for AML research. By using a case study of a major financial institution in the EU-area, a number of technological influences on AML are deconstructed and are used to examine the role that technology plays within AML. The book provides a systems theoretical description of the effects of technology on AML and offers considerations on the risk-based approach - the most important contemporary evolution within regulatory initiatives on AML and terrorism financing. Technology and Anti-Money Laundering will appeal to researchers of financial crime and AML as well as those interested in information systems and systems theory. A number of considerations for practitioners are also discussed, including the risk-based approach and the integration of AML-technology in financial institutions, as well as an important data-mining application. Money Laundering Reporting Officers (MLROs) in financial institutions and central bankers will also find much of interest in this book.

This book consolidates decades of knowledge on space flight navigation theory, which has thus far been spread across various research articles. By gathering this research into a single text, it will be more accessible to students curious about the study of space flight navigation. Books on optimal control theory and orbital mechanics have not adequately explored the field of space flight navigation theory until this point. The opening chapters introduce essential concepts within optimal control theory, such as the optimization of static systems, special boundary conditions, and dynamic equality constraints. An analytical approach is focused on throughout, as opposed to computational. The result is a book that emphasizes simplicity and practicability, which makes it accessible and engaging. This holds true in later chapters that involve orbital mechanics, two-body maneuvers, bounded inputs, and flight in non-spherical gravity fields. The intended audience is primarily upper-undergraduate students, graduate students, and researchers of aerospace, mechanical, and/or electrical engineering. It will be especially valuable to those with interests in spacecraft dynamics and control. Readers should be familiar with basic dynamics and modern control theory. Additionally, a knowledge of linear algebra, variational methods, and ordinary differential equations is recommended.

Hybrid System Identification helps readers to build mathematical models of dynamical systems switching between different operating modes, from their experimental observations. It provides an overview of the interaction between system identification, machine learning and pattern recognition fields in explaining and analysing hybrid system identification. It emphasises the optimization and computational complexity issues that lie at the core of the problems considered and sets them aside from standard system identification problems. The book presents practical methods that leverage this complexity, as well as a broad view of state-of-the-art machine learning methods. The authors illustrate the key technical points using examples and figures to help the reader understand the material. The book includes an in-depth discussion and computational analysis of hybrid system identification problems, moving from the basic questions of the definition of hybrid systems and system identification to methods of hybrid system identification and the estimation of switched linear/affine and piecewise affine models. The authors also give an overview of the various applications of hybrid systems, discuss the connections to other fields, and describe more advanced material on recursive, state-space and nonlinear hybrid system identification. Hybrid System Identification includes a detailed exposition of major methods, which allows researchers and practitioners to acquaint themselves rapidly with state-of-the-art tools. The book is also a sound basis for graduate and undergraduate students studying this area of control, as the presentation and form of the book provides the background and coverage necessary for a full understanding of hybrid system identification, whether the reader is initially familiar with system identification related to hybrid systems or not.

This book is in honor of Yasuhiko Takahara, a first-class researcher who has been active for some 50 years at the global level in systems research. Researchers and practitioners from Japan and other countries who have been influenced by Takahara have come together from far and wide to contribute their major research masterpieces in the field of systems research in the broadest sense. While the roots of Takahara's systems research are in general systems theory and systems control theory, he developed his research and teaching in diverse directions such as management information science, engineering, social simulation, and systems thinking. As a result, many of the researchers and practitioners he supervised or influenced have established their own positions and are now active around the world in a wide range of systems research. Volume I is a collection of their masterpieces or representative works in the field of systems theory and modeling.

This book focuses on most recent theoretical findings on control issues for active suspension systems. The authors first introduce the theoretical background of active suspension control, then present constrained H control approaches of active suspension systems in the entire frequency domain, focusing on the state feedback and dynamic output feedback controller in the finite frequency domain which people are most sensitive to. The book also contains nonlinear constrained tracking control via terminal sliding-mode control and adaptive robust theory, presenting controller design of active suspensions as well as the reliability control of active suspension systems. The target audience primarily comprises research experts in control theory, but the book may also be beneficial for graduate students alike.

This book provides a systematic approach to the design of predictor based controllers for (time-varying) linear systems with either (time-varying) input or state delays. Differently from those traditional predictor based controllers, which are infinite-dimensional static feedback laws and may cause difficulties in their practical implementation, this book develops a truncated predictor feedback (TPF) which involves only finite dimensional static state feedback. Features and topics: A novel approach referred to as truncated predictor feedback for the stabilization of (time-varying) time-delay systems in both the continuous-time setting and the discrete-time setting is built systematically Semi-global and global stabilization problems of linear time-delay systems subject to either magnitude saturation or energy constraints are solved in a systematic manner Both stabilization of a single system and consensus of a group of systems (multi-agent systems) are treated in a unified manner by applying the truncated predictor feedback and predictor feedback The properties of the solutions to a class of parametric (differential and difference) Lyapunov matrix equations are presented in detail Detailed numerical examples and applications to the spacecraft rendezvous and formation flying problems are provided to demonstrate the usefulness of the presented theoretical results This book can be a useful resource for the researchers, engineers, and graduate students in the fields of control, applied mathematics, mechanical engineering, electrical engineering, and aerospace engineering.

This textbook teaches students techniques for the design of advanced digital systems using System-on-Chip (SoC) Field Programmable Gate Arrays (FPGAs). The author demonstrates design of custom hardware components for the FPGA fabric using VHDL, with implementation of custom hardware-software interfaces. Readers gain hands-on experience by writing programs and Linux device drivers in C to interact with custom hardware. This textbook enables laboratory experience in the design of custom digital systems using SoC FPGAs, emphasizing computational tasks such as digital signal processing, audio, or video processing.

Ordinary and fractional approximations by non-additive integrals, especially by integral approximators of Choquet, Silkret and Sugeno types, are a new trend in approximation theory. These integrals are only subadditive and only the first two are positive linear, and they produce very fast and flexible approximations based on limited data. The author presents both the univariate and multivariate cases. The involved set functions are much weaker forms of the Lebesgue measure and they were conceived to fulfill the needs of economic theory and other applied sciences. The approaches presented here are original, and all chapters are self-contained and can be read independently. Moreover, the book's findings are sure to find application in many areas of pure and applied mathematics, especially in approximation theory, numerical analysis and mathematical economics (both ordinary and fractional). Accordingly, it offers a unique resource for researchers, graduate students, and for coursework in the above-mentioned fields, and belongs in all science and engineering libraries.

The purpose of this introductory book is to couple the teaching of chaotic circuit and systems theory with the use of field programmable gate arrays (FPGAs). As such, it differs from other texts on chaos: first, it puts emphasis on combining theoretical methods, simulation tools and physical realization to help the reader gain an intuitive understanding of the properties of chaotic systems. Second, the "medium" used for physical realization is the FPGA. These devices are massively parallel architectures that can be configured to realize a variety of logic functions. Hence, FPGAs can be configured to emulate systems of differential equations. Nevertheless maximizing the capabilities of an FPGA requires the user to understand the underlying hardware and also FPGA design software. This is achieved by the third distinctive feature of this book: a lab component in each chapter. Here, readers are asked to experiment with computer simulations and FPGA designs, to further their understanding of concepts covered in the book. This text is intended for graduate students in science and engineering interested in exploring implementation of nonlinear dynamical (chaotic) systems on FPGAs.

This book aims to bring together the latest innovative knowledge, analysis, and synthesis of fractional control problems of nonlinear systems as well as some related applications. Fractional order systems (FOS) are dynamical systems that can be modelled by a fractional differential equation carried with a non-integer derivative. In the last few decades, the growth of science and engineering systems has considerably stimulated the employment of fractional calculus in many subjects of control theory, for example, in stability, stabilization, controllability, observability, observer design, and fault estimation. The application of control theory in FOS is an important issue in many engineering applications. So, to accurately describe these systems, the fractional order differential equations have been introduced.

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.